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# SPDX-License-Identifier: GPL-2.0-or-later
#
# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
#
# raspberrypi_parser.py - Parser for Raspberry Pi config file format

from .parser import Parser

import json
import numbers

import libtuning.utils as utils


class RaspberryPiParser(Parser):
    def __init__(self):
        super().__init__()

    # The string in the 'disable' and 'plot' lists are formatted as
    # 'rpi.{module_name}'.
    # @brief Enumerate, as a module, @a listt if its value exists in @a dictt
    #        and it is the name of a valid module in @a modules
    def _enumerate_rpi_modules(self, listt, dictt, modules):
        for x in listt:
            name = x.replace('rpi.', '')
            if name not in dictt:
                continue
            module = utils.get_module_by_typename(modules, name)
            if module is not None:
                yield module

    def _valid_macbeth_option(self, value):
        if not isinstance(value, dict):
            return False

        if list(value.keys()) != ['small', 'show']:
            return False

        for val in value.values():
            if not isinstance(val, numbers.Number):
                return False

        return True

    def parse(self, config_file: str, modules: list) -> (dict, list):
        with open(config_file, 'r') as config_json:
            config = json.load(config_json)

        disable = []
        for module in self._enumerate_rpi_modules(config['disable'], config, modules):
            disable.append(module)
            # Remove the disabled module's config too
            config.pop(module.name)
        config.pop('disable')

        # The raspberrypi config format has 'plot' map to a list of module
        # names which should be plotted. libtuning has each module contain the
        # plot information in itself so do this conversion.

        for module in self._enumerate_rpi_modules(config['plot'], config, modules):
            # It's fine to set the value of a potentially disabled module, as
            # the object still exists at this point
            module.appendValue('debug', 'plot')
        config.pop('plot')

        # Convert the keys from module name to module instance

        new_config = {}

        for module_name in config:
            module = utils.get_module_by_type_name(modules, module_name)
            if module is not None:
                new_config[module] = config[module_name]

        new_config['general'] = {}

        if 'blacklevel' in config:
            if not isinstance(config['blacklevel'], numbers.Number):
                raise TypeError('Config "blacklevel" must be a number')
            # Raspberry Pi's ctt config has magic blacklevel value -1 to mean
            # "get it from the image metadata". Since we already do that in
            # Image, don't save it to the config here.
            if config['blacklevel'] >= 0:
                new_config['general']['blacklevel'] = config['blacklevel']

        if 'macbeth' in config:
            if not self._valid_macbeth_option(config['macbeth']):
                raise TypeError('Config "macbeth" must be a dict: {"small": number, "show": number}')
            new_config['general']['macbeth'] = config['macbeth']
        else:
            new_config['general']['macbeth'] = {'small': 0, 'show': 0}

        return new_config, disable
"hl num">245, 243], # white 9.5 [200, 202, 202], # neutral 8 [161, 163, 163], # neutral 6.5 [121, 121, 122], # neutral 5 [82, 84, 86], # neutral 3.5 [49, 49, 51] # black 2 ]) """ convert reference colours from srgb to rgb """ m_srgb = degamma(m_rgb) """ reorder reference values to match how patches are ordered """ m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24, 3)) """ reformat alsc correction tables or set colour_cals to None if alsc is deactivated """ if cal_cr_list is None: colour_cals = None else: colour_cals = {} for cr, cb in zip(cal_cr_list, cal_cb_list): cr_tab = cr['table'] cb_tab = cb['table'] """ normalise tables so min value is 1 """ cr_tab = cr_tab/np.min(cr_tab) cb_tab = cb_tab/np.min(cb_tab) colour_cals[cr['ct']] = [cr_tab, cb_tab] """ for each image, perform awb and alsc corrections. Then calculate the colour correction matrix for that image, recording the ccm and the colour tempertaure. """ ccm_tab = {} for Img in imgs: Cam.log += '\nProcessing image: ' + Img.name """ get macbeth patches with alsc applied if alsc enabled. Note: if alsc is disabled then colour_cals will be set to None and no the function will simply return the macbeth patches """ r, b, g = get_alsc_patches(Img, colour_cals, grey=False) """ do awb Note: awb is done by measuring the macbeth chart in the image, rather than from the awb calibration. This is done so the awb will be perfect and the ccm matrices will be more accurate. """ r_greys, b_greys, g_greys = r[3::4], b[3::4], g[3::4] r_g = np.mean(r_greys/g_greys) b_g = np.mean(b_greys/g_greys) r = r / r_g b = b / b_g """ normalise brightness wrt reference macbeth colours and then average each channel for each patch """ gain = np.mean(m_srgb)/np.mean((r, g, b)) Cam.log += '\nGain with respect to standard colours: {:.3f}'.format(gain) r = np.mean(gain*r, axis=1) b = np.mean(gain*b, axis=1) g = np.mean(gain*g, axis=1) """ calculate ccm matrix """ ccm = do_ccm(r, g, b, m_srgb) """ if a ccm has already been calculated for that temperature then don't overwrite but save both. They will then be averaged later on """ if Img.col in ccm_tab.keys(): ccm_tab[Img.col].append(ccm) else: ccm_tab[Img.col] = [ccm] Cam.log += '\n' Cam.log += '\nFinished processing images' """ average any ccms that share a colour temperature """ for k, v in ccm_tab.items(): tab = np.mean(v, axis=0) tab = np.where((10000*tab) % 1 <= 0.05, tab+0.00001, tab) tab = np.where((10000*tab) % 1 >= 0.95, tab-0.00001, tab) ccm_tab[k] = list(np.round(tab, 5)) Cam.log += '\nMatrix calculated for colour temperature of {} K'.format(k) """ return all ccms with respective colour temperature in the correct format, sorted by their colour temperature """ sorted_ccms = sorted(ccm_tab.items(), key=lambda kv: kv[0]) ccms = [] for i in sorted_ccms: ccms.append({ 'ct': i[0], 'ccm': i[1] }) return ccms """ calculates the ccm for an individual image. ccms are calculate in rgb space, and are fit by hand. Although it is a 3x3 matrix, each row must add up to 1 in order to conserve greyness, simplifying calculation. Should you want to fit them in another space (e.g. LAB) we wish you the best of luck and send us the code when you are done! :-) """ def do_ccm(r, g, b, m_srgb): rb = r-b gb = g-b rb_2s = (rb*rb) rb_gbs = (rb*gb) gb_2s = (gb*gb) r_rbs = rb * (m_srgb[..., 0] - b) r_gbs = gb * (m_srgb[..., 0] - b) g_rbs = rb * (m_srgb[..., 1] - b) g_gbs = gb * (m_srgb[..., 1] - b) b_rbs = rb * (m_srgb[..., 2] - b) b_gbs = gb * (m_srgb[..., 2] - b) """ Obtain least squares fit """ rb_2 = np.sum(rb_2s) gb_2 = np.sum(gb_2s) rb_gb = np.sum(rb_gbs) r_rb = np.sum(r_rbs) r_gb = np.sum(r_gbs) g_rb = np.sum(g_rbs) g_gb = np.sum(g_gbs) b_rb = np.sum(b_rbs) b_gb = np.sum(b_gbs) det = rb_2*gb_2 - rb_gb*rb_gb """ Raise error if matrix is singular... This shouldn't really happen with real data but if it does just take new pictures and try again, not much else to be done unfortunately... """ if det < 0.001: raise ArithmeticError r_a = (gb_2*r_rb - rb_gb*r_gb)/det r_b = (rb_2*r_gb - rb_gb*r_rb)/det """ Last row can be calculated by knowing the sum must be 1 """ r_c = 1 - r_a - r_b g_a = (gb_2*g_rb - rb_gb*g_gb)/det g_b = (rb_2*g_gb - rb_gb*g_rb)/det g_c = 1 - g_a - g_b b_a = (gb_2*b_rb - rb_gb*b_gb)/det b_b = (rb_2*b_gb - rb_gb*b_rb)/det b_c = 1 - b_a - b_b """ format ccm """ ccm = [r_a, r_b, r_c, g_a, g_b, g_c, b_a, b_b, b_c] return ccm